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全球农业分析市场 - 2023-2030 年
市场调查报告书
商品编码
1319250

全球农业分析市场 - 2023-2030 年

Global Agriculture Analytics Market - 2023-2030
出版日期: | 出版商: DataM Intelligence | 英文 190 Pages | 商品交期: 约2个工作天内

价格

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简介目录

市场概述

全球农业分析市场规模在 2022 年达到 12 亿美元，预计到 2030 年将达到 28 亿美元，2023-2030 年的复合年增长率为 11.5%。

农业分析是指利用先进技术、数据分析和预测建模技术，在农业领域获得洞察力并做出明智决策。精准农业需要利用技术和信息，根据具体地点优化耕作方法。它利用传感器、无人机和 GPS 系统等工具，积累有关土壤条件、作物生长和环境因素的记录。

数据驱动型农业是指利用通过传感器、卫星图像和气象站等各种资源收集的农业记录，为农业生产决策提供信息的做法。

人工智能（AI）和机器学习（ML）技术在农业分析中发挥着重要作用。人工智能和 ML 算法用于分析大量农业数据、识别模式并生成预测模型。这些模型有助于预测作物产量、疾病爆发、天气模式以及优化资源分配，从而提高决策和运营效率。

市场动态

全球粮食生产需求的增长推动了农业分析市场的发展

全球人口持续大幅增长。据联合国粮农组织（FAO）预测，谷物产量年增长率将放缓至 0.7%（发展中国家为 0.8%），到 2050 年，谷物平均产量将从 3.2 吨/公顷增至 4.3 吨/公顷左右。根据同一资料来源，预计到 2050 年，世界人口将达到 97 亿。人口激增给农业区带来了压力，需要提供额外的粮食来满足不断增长的需求。

快速的城市化和生活方式的转变带来了从传统的生计农业向商业农业的转变。这种转变要求改进农业实践，采用先进技术，包括农业分析技术，以提高粮食生产的生产力和绩效。

物联网（IoT）与传感器技术的融合有望促进农业分析市场的发展

物联网和传感器技术在农业领域的结合，实现了对土壤湿度、温度、湿度和作物健康等众多参数的独特跟踪和控制。根据世界经济论坛的一份报告，在农业中采用物联网技术可使用水量减少 10-15%，化学投入减少 20-30%。这些技术提供的实时统计数据可通过农业分析进行分析，使农民能够优化资源配置，减少浪费，并装饰常见的农业性能。

物联网和传感器技术可以从农田、牲畜和机械中持续收集数据。这些实时数据可通过农业分析工具进行分析，为决策提供有价值的见解。例如，传感器可以提供土壤湿度数据，使农民能够精确安排灌溉时间。通过实时数据分析，可以主动应对不断变化的条件，从而改进农场管理方法，提高生产率。

缺乏认识和技术专长阻碍了农业分析市场的发展

农业分析需要一定的数据分析、解释和分析工具使用方面的专业技术知识。然而，许多农民和农业专业人员可能不具备这些方面的必要技能或知识。他们可能不熟悉统计分析、建模技术和数据可视化。这种知识差距会阻碍农业分析解决方案的实施和有效利用。

农民在获取提供农业分析指导的培训计划或支持系统方面往往面临挑战。培训资源、研讨会或专家援助有限，会阻碍数据分析技术专业知识的发展和分析工具的实际应用。缺乏可利用的培训和支持机制加剧了技术专长方面的差距。

COVID-19 影响分析

COVID-19 分析包括 COVID 前情景、COVID 情景和 COVID 后情景，以及定价动态（包括大流行期间和之后的定价变化，并与 COVID 前情景进行比较）、供求光谱（由于交易限制、封锁和后续问题而导致的供求变化）、政府倡议（政府机构为振兴市场、部门或行业而采取的倡议）和制造商战略倡议（此处将涵盖制造商为缓解 COVID 问题而采取的措施）。

目 录

第 1 章：研究方法与范围

  • 研究方法
  • 报告的研究目标和范围

第2章：市场定义与概述

第3章：执行摘要

  • 按组件分類的市场摘要
  • 按应用分類的市场摘要
  • 按部署分類的市场片段
  • 按农场规模分類的市场片段
  • 按地区分類的市场摘要

第 4 章：市场动态

  • 市场影响因素
    • 驱动因素
    • 限制因素
    • 机会
    • 影响分析

第 5 章：行业分析

  • 波特五力分析
  • 供应链分析
  • 定价分析
  • 监管分析

第 6 章：COVID-19 分析

  • COVID-19 分析
    • COVID-19 之前的情况
    • COVID-19 期间的情景
    • COVID-19 后的情况
  • COVID-19 期间的定价动态
  • 供求关系
  • 大流行期间与市场相关的政府倡议
  • 制造商的战略倡议
  • 结论

第 7 章：按组件分类

  • 解决方案
  • 服务

第 8 章：按应用分类

  • 农场分析
  • 牲畜分析
  • 水产养殖
  • 其他应用

第 9 章：按部署

  • 云计算
  • 内部部署

第 10 章：按养殖场规模

  • 大型农场
  • 中小型农场

第 11 章：按地区划分

  • 北美洲
    • 美国
    • 加拿大
    • 墨西哥
  • 欧洲
    • 德国
    • 英国
    • 法国
    • 意大利
    • 西班牙
    • 欧洲其他地区
  • 南美洲
    • 巴西
    • 阿根廷
    • 南美洲其他地区
  • 亚太地区
    • 中国
    • 印度
    • 日本
    • 澳大利亚
    • 亚太其他地区
  • 中东和非洲

第 12 章 ：竞争格局

  • 竞争格局
  • 市场定位/份额分析
  • 合併与收购分析

第 13 章 ：公司简介

  • Trimble Inc.
    • 公司概况
    • 产品组合和说明
    • 财务概况
    • 主要发展
  • Bayer AG
  • IBM Corporation
  • Deere & Company
  • Ageagle Aerial Systems Inc
  • Vistex, Inc.
  • Agrivi
  • SAS Institute Inc.
  • Conservis Corporation
  • Iteris Inc

第 14 章：附录

简介目录
Product Code: AG6574

Market Overview

Global Agriculture Analytics Market reached US$ 1.2 billion in 2022 and is expected to reach US$ 2.8 billion by 2030 growing with a CAGR of 11.5% during the forecast period 2023-2030.

Agriculture analytics refers to the use of advanced technologies, data analysis, and predictive modeling techniques to gain insights and make informed decisions in the field of agriculture. Precision agriculture entails the use of technology and information to optimize farming practices on a site-specific basis. It utilizes tools along with sensors, drones, and GPS systems to accumulate records about soil conditions, crop growth, and environmental factors.

Data-driven farming refers to the practice of using agricultural records, collected through various resources inclusive of sensors, satellite imagery, and weather stations, to inform decision-making in farming operations.

Artificial intelligence (AI) and machine studying (ML) techniques are playing a important role in agriculture analytics. AI and ML algorithms are used to analyze large volumes of agricultural data, identify patterns, and generate predictive models. These models assist in predicting crop yields, disorder outbreaks, weather patterns, and optimizing resource allocation for improved decision-making and operational efficiency.

Market Dynamics

Increasing Demand for Food Production Globally is Driving the Agriculture Analytics Market

The global population continues to increase at a substantial rate. According to the Food and Agriculture Organization (FAO), cereal yield growth would slowdown to 0.7 percent per annum (0.8 percent in developing countries), and average cereal yield would by 2050 reach around 4.3 ton/ha, up from 3.2 ton/ha. According to the same source, the world population is projected to reach 9.7 billion by 2050. This population boom puts pressure at the agriculture zone to supply extra food to fulfill the rising demand.

Rapid urbanization and converting life have brought about a shift from conventional subsistence farming to commercial agriculture. This shift necessitates improved agricultural practices and the adoption of advanced technology, together with agriculture analytics, to boom productivity and performance in food production.

Integration of Internet of Things (IoT) and Sensor Technologies is Expected to Foster the Agriculture Analytics Market

The mixing of IoT and sensor technology in agriculture enables unique tracking and control of numerous parameters such as soil moisture, temperature, humidity, and crop health. According to a report by the World Economic Forum, the adoption of IoT in agriculture can lead to a 10-15% reduction in water usage and a 20-30% reduction in chemical inputs. Those technologies provide real-time statistics that may be analyzed through agriculture analytics, allowing farmers to optimize resource allocation, reduce waste, and decorate common farming performance.

IoT and sensor technology enable continuous data collection from agricultural fields, livestock, and machinery. This real-time data can be analyzed using agriculture analytics tools to provide valuable insights for decision-making. For instance, sensors can provide data on soil moisture levels, allowing farmers to precisely schedule irrigation. Real-time data analysis enables proactive responses to changing conditions, leading to improved farm management practices and increased productivity.

Lack of Awareness and Technical Expertise is Holding Back the Agriculture Analytics Market

Agriculture analytics requires a certain level of technical expertise in data analysis, interpretation, and the use of analytics tools. However, many farmers and agricultural professionals may not possess the necessary skills or knowledge in these areas. They may lack familiarity with statistical analysis, modeling techniques, and data visualization. This knowledge gap can impede the implementation and effective utilization of agriculture analytics solutions.

Farmers often face challenges in accessing training programs or support systems that provide guidance on agriculture analytics. Limited availability of training resources, workshops, or expert assistance can hinder the development of technical expertise in data analysis and the practical application of analytics tools. The lack of accessible training and support mechanisms exacerbates the gap in technical expertise.

COVID-19 Impact Analysis

The COVID-19 Analysis includes Pre-COVID Scenario, COVID Scenario, and Post-COVID Scenario along with Pricing Dynamics (Including pricing change during and post-pandemic comparing it with pre-COVID scenarios), Demand-Supply Spectrum (Shift in demand and supply owing to trading restrictions, lockdown, and subsequent issues), Government Initiatives (Initiatives to revive market, sector or Industry by Government Bodies) and Manufacturers Strategic Initiatives (What manufacturers did to mitigate the COVID issues will be covered here).

Segment Analysis

The global agriculture analytics market is segmented based on source, packaging, distribution channel, and region.

By Deployment, the Cloud Segment is Estimated to have Significant Growth During the Forecast Period

Cloud-based solutions offer scalability and flexibility, allowing users to scale their storage and computing resources based on their needs. This scalability is particularly valuable in the agriculture industry, where data volumes can vary significantly throughout the agricultural cycle. According to a journal published by Frontiers, Automation and the use of artificial intelligence (AI), internet of things (IoT), drones, robots, and Big Data serve as a basis for a global "Digital Twin," which will contribute to the development of site-specific conservation and management practices that will increase incomes and global sustainability of agricultural systems.

Cloud-based agriculture analytics platforms can accommodate the storage and processing requirements of large and diverse agricultural datasets. The cloud enables easy access to data from anywhere, anytime, as long as there is an internet connection. This accessibility promotes collaboration and data sharing among stakeholders in the agriculture ecosystem, including farmers, researchers, consultants, and agribusinesses. Cloud-based platforms facilitate real-time data access, analytics, and collaborative decision-making, contributing to the overall adoption and dominance of the cloud segment.

Geographical Analysis

Asia Pacific is the Fastest Growing Market in the Agriculture Analytics Market

Asia Pacific is domestic to a large agricultural area and a vast population engaged in farming. The increasing demand for meals, coupled with the need to beautify agricultural productiveness, has led to the adoption of superior technology and analytics solutions. According to Asia Development Bank, with 76% of Asia's poor living in rural areas, raising agricultural productivity and income is key to fighting poverty. By leveraging agriculture analytics, farmers in the region can optimize resource utilization, implement precision farming practices, and improve overall productivity.

Precision agriculture techniques, which heavily rely upon data-driven insights and analytics, have received traction inside the Asia Pacific region. Farmers are increasingly more adopting technology inclusive of sensors, drones, and satellite imagery to monitor crops, analyze soil conditions, and optimize resource management. Agriculture analytics performs a important role in analyzing the collected data and supplying actionable insights for precision agriculture, contributing to the market growth inside the region.

Competitive Landscape

The major global players in the market include: Trimble Inc., Bayer AG, IBM Corporation, Deere & Company, Ageagle Aerial Systems Inc, Vistex, Inc., Agrivi, SAS Institute Inc., Conservis Corporation, and Iteris Inc.

Why Purchase the Report?

  • To visualize the global agriculture analytics market segmentation based on component, application, deployment, farm size, and region, as well as understand key commercial assets and players.
  • Identify commercial opportunities in the market by analyzing trends and co-development.
  • Excel data sheet with numerous data points of agriculture analytics market-level with all segments.
  • The PDF report consists of cogently put-together market analysis after exhaustive qualitative interviews and in-depth market study.
  • Product mapping is available as Excel consists of key products of all the major market players.

The global agriculture analytics market report would provide approximately 69 tables, 65 figures and 190 Pages.

Target Audience 2023

  • Manufacturers/ Buyers
  • Industry Investors/Investment Bankers
  • Research Professionals
  • Emerging Companies

Table of Contents

1. Methodology and Scope

  • 1.1. Research Methodology
  • 1.2. Research Objective and Scope of the Report

2. Market Definition and Overview

3. Executive Summary

  • 3.1. Market Snippet, by Component
  • 3.2. Market Snippet, by Application
  • 3.3. Market Snippet, by Deployment
  • 3.4. Market Snippet, by Farm Size
  • 3.5. Market Snippet, by Region

4. Market Dynamics

  • 4.1. Market Impacting Factors
    • 4.1.1. Drivers
    • 4.1.2. Restraints
    • 4.1.3. Opportunity
    • 4.1.4. Impact Analysis

5. Industry Analysis

  • 5.1. Porter's Five Force Analysis
  • 5.2. Supply Chain Analysis
  • 5.3. Pricing Analysis
  • 5.4. Regulatory Analysis

6. COVID-19 Analysis

  • 6.1. Analysis of COVID-19
    • 6.1.1. Scenario Before COVID-19
    • 6.1.2. Scenario During COVID-19
    • 6.1.3. Scenario Post COVID-19
  • 6.2. Pricing Dynamics Amid COVID-19
  • 6.3. Demand-Supply Spectrum
  • 6.4. Government Initiatives Related to the Market During Pandemic
  • 6.5. Manufacturers Strategic Initiatives
  • 6.6. Conclusion

7. By Component

  • 7.1. Introduction
    • 7.1.1. Market Size Analysis and Y-o-Y Growth Analysis (%), By Component
    • 7.1.2. Market Attractiveness Index, By Component
  • 7.2. Solution*
    • 7.2.1. Introduction
    • 7.2.2. Market Size Analysis and Y-o-Y Growth Analysis (%)
  • 7.3. Services

8. By Application

  • 8.1. Introduction
    • 8.1.1. Market Size Analysis and Y-o-Y Growth Analysis (%), By Application
    • 8.1.2. Market Attractiveness Index, By Application
  • 8.2. Farm Analytics*
    • 8.2.1. Introduction
    • 8.2.2. Market Size Analysis and Y-o-Y Growth Analysis (%)
  • 8.3. Livestock Analytics
  • 8.4. Aquaculture
  • 8.5. Others

9. By Deployment

  • 9.1. Introduction
    • 9.1.1. Market Size Analysis and Y-o-Y Growth Analysis (%), By Deployment
    • 9.1.2. Market Attractiveness Index, By Deployment
  • 9.2. Cloud*
    • 9.2.1. Introduction
    • 9.2.2. Market Size Analysis and Y-o-Y Growth Analysis (%)
  • 9.3. On-Premises

10. By Farm Size

  • 10.1. Introduction
    • 10.1.1. Market Size Analysis and Y-o-Y Growth Analysis (%), By Farm Size
    • 10.1.2. Market Attractiveness Index, By Farm Size
  • 10.2. Large Farms*
    • 10.2.1. Introduction
    • 10.2.2. Market Size Analysis and Y-o-Y Growth Analysis (%)
  • 10.3. Small & Medium Farms

11. By Region

  • 11.1. Introduction
    • 11.1.1. Market Size Analysis and Y-o-Y Growth Analysis (%), By Region
    • 11.1.2. Market Attractiveness Index, By Region
  • 11.2. North America*
    • 11.2.1. Introduction
    • 11.2.2. Key Region-Specific Dynamics
    • 11.2.3. Market Size Analysis and Y-o-Y Growth Analysis (%), By Component
    • 11.2.4. Market Size Analysis and Y-o-Y Growth Analysis (%), By Application
    • 11.2.5. Market Size Analysis and Y-o-Y Growth Analysis (%), By Deployment
    • 11.2.6. Market Size Analysis and Y-o-Y Growth Analysis (%), By Farm Size
    • 11.2.7. Market Size Analysis and Y-o-Y Growth Analysis (%), By Country
      • 11.2.7.1. The U.S.
      • 11.2.7.2. Canada
      • 11.2.7.3. Mexico
  • 11.3. Europe
    • 11.3.1. Introduction
    • 11.3.2. Key Region-Specific Dynamics
    • 11.3.3. Market Size Analysis and Y-o-Y Growth Analysis (%), By Component
    • 11.3.4. Market Size Analysis and Y-o-Y Growth Analysis (%), By Application
    • 11.3.5. Market Size Analysis and Y-o-Y Growth Analysis (%), By Deployment
    • 11.3.6. Market Size Analysis and Y-o-Y Growth Analysis (%), By Farm Size
    • 11.3.7. Market Size Analysis and Y-o-Y Growth Analysis (%), By Country
      • 11.3.7.1. Germany
      • 11.3.7.2. The U.K.
      • 11.3.7.3. France
      • 11.3.7.4. Italy
      • 11.3.7.5. Spain
      • 11.3.7.6. Rest of Europe
  • 11.4. South America
    • 11.4.1. Introduction
    • 11.4.2. Key Region-Specific Dynamics
    • 11.4.3. Market Size Analysis and Y-o-Y Growth Analysis (%), By Component
    • 11.4.4. Market Size Analysis and Y-o-Y Growth Analysis (%), By Application
    • 11.4.5. Market Size Analysis and Y-o-Y Growth Analysis (%), By Deployment
    • 11.4.6. Market Size Analysis and Y-o-Y Growth Analysis (%), By Farm Size
    • 11.4.7. Market Size Analysis and Y-o-Y Growth Analysis (%), By Country
      • 11.4.7.1. Brazil
      • 11.4.7.2. Argentina
      • 11.4.7.3. Rest of South America
  • 11.5. Asia-Pacific
    • 11.5.1. Introduction
    • 11.5.2. Key Region-Specific Dynamics
    • 11.5.3. Market Size Analysis and Y-o-Y Growth Analysis (%), By Component
    • 11.5.4. Market Size Analysis and Y-o-Y Growth Analysis (%), By Application
    • 11.5.5. Market Size Analysis and Y-o-Y Growth Analysis (%), By Deployment
    • 11.5.6. Market Size Analysis and Y-o-Y Growth Analysis (%), By Farm Size
    • 11.5.7. Market Size Analysis and Y-o-Y Growth Analysis (%), By Country
      • 11.5.7.1. China
      • 11.5.7.2. India
      • 11.5.7.3. Japan
      • 11.5.7.4. Australia
      • 11.5.7.5. Rest of Asia-Pacific
  • 11.6. Middle East and Africa
    • 11.6.1. Introduction
    • 11.6.2. Key Region-Specific Dynamics
    • 11.6.3. Market Size Analysis and Y-o-Y Growth Analysis (%), By Component
    • 11.6.4. Market Size Analysis and Y-o-Y Growth Analysis (%), By Application
    • 11.6.5. Market Size Analysis and Y-o-Y Growth Analysis (%), By Deployment
    • 11.6.6. Market Size Analysis and Y-o-Y Growth Analysis (%), By Farm Size

12. Competitive Landscape

  • 12.1. Competitive Scenario
  • 12.2. Market Positioning/Share Analysis
  • 12.3. Mergers and Acquisitions Analysis

13. Company Profiles

  • 13.1. Trimble Inc.*
    • 13.1.1. Company Overview
    • 13.1.2. Product Portfolio and Description
    • 13.1.3. Financial Overview
    • 13.1.4. Key Developments
  • 13.2. Bayer AG
  • 13.3. IBM Corporation
  • 13.4. Deere & Company
  • 13.5. Ageagle Aerial Systems Inc
  • 13.6. Vistex, Inc.
  • 13.7. Agrivi
  • 13.8. SAS Institute Inc.
  • 13.9. Conservis Corporation
  • 13.10. Iteris Inc

LIST NOT EXHAUSTIVE

14. Appendix

  • 14.1. About Us and Services
  • 14.2. Contact Us